Clinical Characteristics and Predictors of Mortality in Elderly Patients Hospitalized with COVID-19 in Bangladesh: A Multicenter, Retrospective Study

Purpose Elderly patients are at high risk of fatality from COVID-19. The present work aims to describe the clinical characteristics of elderly inpatients with COVID-19 and identify the predictors of in-hospital mortality at admission. Materials and Methods In this retrospective, multicenter cohort study, we included elderly COVID-19 inpatients (n = 245) from four hospitals in Sylhet, Bangladesh, who had been discharged between October 2020 and February 2021. Demographic, clinical, and laboratory data were extracted from hospital records and compared between survivors and nonsurvivors. We used univariable and multivariable logistic regression analysis to explore the risk factors associated with in-hospital death. Principal Results. Of the included patients, 202 (82.44%) were discharged and 43 (17.55%) died in hospital. Except hypertension, other comorbidities like diabetes, chronic kidney disease, ischemic heart disease, and chronic obstructive pulmonary disease were more prevalent in nonsurvivors. Nonsurvivors had a higher prevalence of leukocytosis (51.2 versus 30.7; p=0.01), lymphopenia (72.1 versus 55; p=0.05), and thrombocytopenia (20.9 versus 9.9; p=0.07). Multivariable regression analysis showed an increasing odds ratio of in-hospital death associated with older age (odds ratio 1.05, 95% CI 1.01–1.10, per year increase; p=0.009), thrombocytopenia (OR = 3.56; 95% CI 1.22–10.33, p=0.019), and admission SpO2 (OR 0.91, 95% CI 0.88–0.95; p=0.001). Conclusions Higher age, thrombocytopenia, and lower initial level of SpO2 at admission are predictors of in-hospital mortality in elderly patients with COVID-19.


Background
Since December 2019, countries all over the world have been confronted with an unprecedented challenge, a battle against the severe acute respiratory syndrome coronavirus (SARS-CoV-2), with a high fatality rate. In the majority of patients, coronavirus disease 2019 (COVID- 19) causes only mild-tomoderate illness with respiratory and flu-like symptoms [1]. However, it has been reported to be severe and critical in 14% and 5% of patients, respectively, and requires intensive care support with mechanical ventilation [2]. e mortality in the critical group of patients is high [3]. Since the beginning of the SARS-CoV-2 outbreak, it was evident that older people, compared to younger ones, were at higher risk of getting the infection and developing more severe diseases with unfavorable prognosis [1,4,5]. Data from China and Italy suggest a case fatality of 2.3% in patients with COVID-19. Case fatalities in Italy appear to be in the elderly age groups of 60 and above, whereas more than 50% of the fatalities in China are in ages greater than 50 [6]. However, the reasons why older people are at significantly increased risk of severe disease following infection from  are not clear. Compared to younger patients, a lesser percentage of elderly patients manifest the classical triad of the disease (fever, cough, and dyspnea) this makes an earlier diagnosis of COVID-19 in these patients difficult and delayed, which may contribute to increased mortality [7,8] Importantly, higher prevalence of comorbidity which is linked to severe disease course and poor prognosis may pose elderly at more risk than younger group [9]. Moreover, immunosenescence and malnutrition can synergistically contribute to the augmented susceptibility and worse outcome of aged people to SARS-CoV-2 [10].
Detection of risk factors for mortality is an important component of the strategies for managing COVID-19. is information is more important at a time when the demand for critical care is upsurging, and the resources for healthcare are limited. Keeping this in mind, we aim to identify the risk factors for in-hospital mortality at admission in the elderly COVID-19 patients. We considered clinical aspects, presence of comorbidities, and laboratory parameters as well as in-hospital outcomes.

Study Methods and Data Collection.
Data were extracted from the hospital records of elderly patients (age >60 years) who had been admitted with a diagnosis of COVID-19 in four COVID-19 designated hospitals in Sylhet, Bangladesh (a major city in north-eastern Bangladesh), during the pandemic crisis of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) between October 2020 and February 2021.
ose without a definitive outcome during their hospitalization were excluded from the study. Subjects with missing data in the records were also excluded. Clinical, demographic, and laboratory data from all patients were recorded. e clinical diagnosis of COVID-19 was made when patients met one of the two following criteria: (I) a positive RT-PCR for SARS-CoV-2 or (II) pulmonary abnormality characteristics of COVID-19 found on chest X-ray or chest CTscan based on the radiological criteria of COVID-9 infection. As flowchart of the research process is an important part of the scientific article [11], we have given it in Figure 1.

Study Variables.
e outcome variable was in-hospital death (nonsurvivors and survivors), a binary variable. e demographic data included here are age, sex, and length of hospital stay in days (LOS). Clinical data included here are clinical features (fever, cough, respiratory distress, fatigability, loss of smell, diarrhea, sore throat, anorexia, and chest pain); the presence of comorbidities like hypertension, chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), diabetes mellitus (DM), ischemic heart disease (IHD), cerebrovascular accident (CVA), and peripheral capillary oxygen saturation (SpO 2 ) at admission; types of respiratory support required (not required supplemental oxygen (No O 2 ), low flow (<4 liters/minute), high flow nasal cannula (HFNC), and noninvasive ventilation (NIV), and those required invasive mechanical ventilator support (ventilator)). Laboratory parameters included complete blood count (CBC), D-dimer, S. ferritin, and blood glucose (BG). e radiographic findings included are chest CT scan reports.

Statistical Analysis.
Patients' demographic and clinical characteristics were analyzed using descriptive statistics. Continuous and categorical variables are expressed as medians (interquartile ranges) or mean (standard deviation (SD)) and as frequency (%), respectively. e Shapiro-Wilk test was used to assess the normality of continuous variables. We presented continuous measurements by the mean and standard deviation (SD) for data that followed a normal distribution and by the median and interquartile range (IQR) for data that were skewed. Patients were included in either the survivor or nonsurvivor group. e mean difference between two groups (survivor versus nonsurvivor) in a continuous variable was assessed using a two-independent-sample mean test (t-test) for the normally distributed data and using nonparametric Mann-Whitney U test for the nonnormally distributed data. e Chi-square test (Χ 2 test) of independence was used to determine the association (difference) among categorical variables. A multiple logistic regression model was used to identify the risk factors for in-hospital death. e candidate predictors for the final model were selected based on clinical relevance and by performing standard model building procedures (backward selection and least AIC value). Initially, simple logistic regression models were fitted for each of the candidate predictors. e factors that were significantly associated with in-hospital death in the simple logistic regression models (p < 0.05) were included in the final multiple logistic regression model. e variables that were highly correlated or associated with each other were excluded from the model due to multicollinearity. Goodness of fit of the model was assessed using the Hosmer-Lemeshow test and area under the ROC curve (AUC). Model findings were presented using odds ratio (OR) and 95% confidence interval (CI). A p value <0.05 was considered statistically significant. Analysis was performed using R software. is study is reported following the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) [12] statements.

Statement of Principal Findings.
In this study, we summarized the clinical and laboratory characteristics of elderly patients (n � 245) diagnosed with COVID-19. We identified predictors of COVID-19-related deaths. Nonsurvivors were older and had a higher prevalence of comorbidities than survivors. A higher proportion of  patients in the nonsurvivor group had leukocytosis, neutrophilia, lymphocytopenia, and thrombocytopenia as well as higher levels of blood glucose, D-dimer, and ferritin. Nonsurvivors were admitted with a more severe degree of hypoxemia than survivors (<0.001). Advanced respiratory support was required more frequently in nonsurvivors. After adjustment for potential covariates, higher age, thrombocytopenia, and lower initial SpO 2 were found to be independently associated with in-hospital mortality.

Strengths and Limitations.
Considering the vulnerability of older people to COVID-19, there is an urgent need to identify predictors of poor outcomes in this group of patients. e developing countries with their limited healthcare resources are badly affected by the challenges placed by the COVID-19. Prioritizing the resources to the vulnerable groups is one of the best options to address this issue and reduce the death toll. is study will contribute in this regard. Here, we studied demographic and clinical characteristics as well as commonly done and cost-effective hematological tests and analyzed their ability to predict the prognosis of patients. We collected data from four COVID-19 designated hospitals in Sylhet. We believe that our study sample is representative of hospitalized elderly patients in Bangladesh.
is study has some limitations. First, this is a retrospective study focused on hospitalized patients. Hospitalized patients usually present with severe disease and consequently have a higher mortality rate, and that is why our data may overestimate overall mortality in the entirety of the older patients with COVID-19. Second, some patients did not have laboratory data for some biochemical values, such as procalcitonin, LDH, lactate, and interleukin-6 serum levels, which may have led to an underestimation of their potential predictive value. Moreover, we could not collect any frailty scale data in this study. In geriatric patients, the level of frailty has been reported to be a useful predictor of short-term COVID-19 outcomes [13].

Interpretation in the Context of the Wider Literature.
Most studies published so far have demonstrated a higher risk of worse outcomes following COVID-19 disease in elderly people. e higher the age, the higher the case fatality rate [14]. We found a case fatality rate in hospitalized patients ≥60 years old of age (17.5%) that was similar to a recent systematic review and meta-analysis [15] but higher than what was reported in China [16] and lower than that reported from a long-term care facility in Italy [17]. Regional variation in case fatality may be due to heterogeneity in testing and reporting system [18], difference in healthcare delivery system [19], genetic variability [20,21], environmental factors [22], and notably virus strains [23]. But what makes elderly people more vulnerable to COVID-19 infection is not a clear/a matter of debate. Overexpression of ACE-2, immune alteration in the elderly, mitochondrial dysfunction, decreased physical activity, hormonal changes, and poor nutrition all may contribute to increased susceptibility to severe disease and death in the elderly [24]. e co-occurrence of chronic diseases in the elderly is increasingly becoming one of the most pressing public health concerns in most of the world. It was reported that more than half of the elderly in developed countries had more than three chronic diseases, meaning that an individual suffers from two or more diseases with different pathology and no mutual dependence at the same time [25,26].
Regardless of ethnicities, the presence of comorbidities significantly increases the chance of contracting the disease and the risk of developing the severe disease with poor outcomes [27]. is is evidenced in both hospitalized patients [28][29][30] and a recent population-based cohort study [31]. Moreover, the elderly patient has a higher burden of comorbidities than nonelderly patients, which could explain the poor prognosis in this group of patients. is present study noticed a higher proportion of comorbidities, such as diabetes mellitus, ischemic heart disease, chronic kidney disease, and chronic obstructive pulmonary disease in nonsurvivors which is in good agreement with other studies [27,32]. Hence, clinicians should treat them with more attention considering high-risk groups.
ough COVID-19 is a respiratory infection, it has a significant impact on the hematopoietic system and hemostasis. Hematologic consequences of this new infection have prompted the medical community to think about new treatment approaches. Changes in peripheral blood cell counts have been well-studied in COVID-19. e most notable features are an increase in the counts of white blood cells and neutrophils, whereas counts of lymphocyte and platelet decrease [33,34]. In several recent meta-analyses, that included geriatric patients, low lymphocyte count, low platelet count, and high neutrophil count, were found to correlate significantly with mortality [33,35,36].
Our study found a lower lymphocyte count in nonsurvivors which is consistent with other studies [33,34]. e possible mechanism of lymphocytopenia is the direct cytotoxic effect of the virus on lymphocytes as there is evidence that ACE-2 receptors are expressed on lymphocytes, direct damage of lymphatic organs, and inflammatory cytokines that continued to be disordered, perhaps leading to lymphocyte apoptosis [37].
Similar to existing studies [33][34][35][36], this study also observed a lower platelet count in nonsurvivors and identified thrombocytopenia as a risk factor for mortality in elderly COVID-19 patients. e potential reasons for thrombocytopenia include the direct effect of SARS-CoV-2 on platelet production, autoimmune destruction of platelets, or increased platelet consumption. Secondary hemophagocytic lymphohistiocytosis causes excessive proliferation and activation of macrophages and, in turn, produces a surge in inflammatory cytokines. It has been postulated that this cytokine storm damages hematopoietic progenitors and reduces platelet production [38,39].
A systematic review and meta-analysis of 78 studies revealed that, increased total WBC count found on admission was a risk factor for mortality and a stepwise increase in risk for mortality in parallel with the increase of the total WBC threshold. Increased baseline absolute neutrophil count (ANC) was found to be a risk factor for intensive care requirements [40]. Consequently, a high neutrophil-tolymphocyte ratio (NLR) on admission was associated with severe COVID-19 and mortality [41]. Neutrophilia may be due to COVID-19-associated immune dysregulation that leads to neutrophil production. Additionally, neutrophilia can be secondary to a superimposed bacterial infection, which is more likely to occur in patients with severe disease [42]. Consistent with these findings, our study also found a higher leukocyte and neutrophil count and a lower lymphocyte and platelet count in nonsurvivors. Also, a significantly higher value of NLR was observed in nonsurvivors of this study.
COVID-19 has been described as a thromboinflammatory syndrome. In patients who developed severe COVID-19, several conditions, including sepsis, complement activation, cytokine storm, endothelial damage, and inflammatory and microthrombotic pathway activation, predispose patients to thrombosis and coagulopathy. D-dimer is a well-known marker for evaluating thrombotic events.
e frequency of D-dimer elevation has been reported to be 36-43% in COVID-19 patients [43]. Patients with elevated D-dimer levels had 1.58 times higher risk for progression to more severe clinical status [44]. In addition, it was also found that the D-dimer levels were higher in nonsurviving patients compared to surviving patients, and also patients with elevated D-dimer levels had a 1.82-fold higher risk for mortality compared to other patients [45]. Consistent with this, our study found a higher level of D-dimer in nonsurvivors when compared with survivors. But this study did not find D-dimer as a risk factor for mortality.
Ferritin, an acute phase reactant, may be a mediator of immune dysregulation in COVID-19 [46]. ere is a complex interplay between ferritin and cytokines. e various suggested mechanisms of raised ferritin levels are the proinflammatory cytokines like IL-16 and TNF-α promoting synthesis of ferritin and leakage of intracellular ferritin by cellular damage [47]; on the other hand, ferritin can induce the expression of pro-and anti-inflammatory cytokines as well. In this regard, there are arguments in favor of adding COVID-19 to the spectrum of hyperferritinemic syndrome [48]. Numerous systematic reviews and meta-analyses observed a high ferritin level in severe disease, and ferritin was found to be a prognostic factor [49,50]. But data from two Italian COVID-19 units demonstrated that ferritin levels over the 25th percentile were associated with a more severe pulmonary involvement, independently of age and gender, and not associated with disease outcomes [51]. is present study found a higher level of ferritin in patients who died but did not find it as a predictor of mortality which agrees with the Italian cohort [52].
ere is a bidirectional relationship between COVID-19 and hyperglycemia.
e high blood glucose level at admission is associated with severe disease and poor outcomes [52][53][54]. On the other hand, COVID-19 is associated with new-onset hyperglycemia or diabetes as well as worsening of preexisting diabetes [55,56]. Possible mechanisms of hyperglycemia in COVID-19 are direct virus-mediated betacell damage, triggering of beta-cell autoimmunity by the virus, disorganized and exuberant immune response against the virus, which leads to perturbations in glycemic status, and iatrogenic hyperglycemia caused by corticosteroids [57]. Our study finding is also consistent with these and we found, compared to survivors, nonsurvivor had a higher blood glucose levels at admission.
Many patients, particularly the elderly who later develop respiratory failure, experience hypoxemia and hypocapnia without signs of respiratory distress. is is called "happy hypoxemia" or "silent hypoxemia." Earlier, it was described in patients during the initial Wuhan outbreak [58], and a recent study found this silent hypoxemia as a poor prognostic marker in COVID-19 [59]. At admission, objective signs of respiratory compromise such as oxygen saturation and respiratory rate are associated with markedly elevated mortality [60]. is present study found a significantly lower SpO 2 level at admission in nonsurvivors when compared with survivors. After adjusting for potential covariates, admission SpO 2 was found to be an independent predictor of mortality.
is present study observed that a higher proportion of patients in the nonsurvivor group have bilateral involvement in chest imaging in comparison to the survivor. is finding is in good agreement with studies reported by Pan et al. [61] and Li et al. [62].

Implications for Policy, Practice, and Future Research.
ese pieces of evidence advocate for judicious decisions on resource allocation in overwhelmed healthcare systems. As per this study's findings concern, patient characteristics, including age, platelet count, and oxygen saturation status at admission, may be significant predictors of death in elderly patients with COVID-19. As COVID-19 is an evolving disease and its course is unpredictable, therefore, elderly patients need special attention and care. To curb the outbreak and reduce the pressures on the healthcare system, it is important that policy-makers must prioritize the high-risk group in their strategic planning. Further larger studies are necessary to better understand and confirm our findings, to rapidly identify characteristics associated with a poor outcome among elderly patients suffering from COVID-19 and provide better care.
Data Availability e datasets analyzed during the current study are not publicly available because of having no permission from the hospitals from where data were collected.

Ethical Approval
Data confidentiality and patient anonymity were maintained at all times. Patient identifiable information was deleted before the database was analyzed; thus, it is not possible to identify patients on an individual level either in this paper or in the database. is study was carried out in accordance with the Declaration of Helsinki and was approved by the Institutional Research Ethics Committees of Sylhet Women's Medical College, Sylhet, Bangladesh, and the committee waived the need for consent.
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